R is a language and an environment for working with data.
It has a large community of users and developers, and many specialized packages.
We will primarily work with data by writing R code.
If every step of your analysis is recorded in an R script, with no manual steps:
Programming is an essential part of reproducible research.
R is open-source and free, so others can use your code without any barriers.
Diagram from “R for Data Science” book (https://r4ds.hadley.nz/)
Diagram from “R for Data Science” book (https://r4ds.hadley.nz/)
Model could mean:
Diagram from “R for Data Science” book (https://r4ds.hadley.nz/)
Modelling goes poorly in a vacuum!
Visualization is critical to identify problems or make sure you are asking the right question.
And first you need to load and tidy your data.
And finally you need to communicate your results!
(Do the workshop)
We’ve covered loading, touched on tidying, done some visualization and a little modelling (or at least summarization).
You still need to learn to communicate your results, with your colleagues or the wider world!
Learning programming in R will super-charge your abilities!